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ONLINE COURSE – Machine Learning with R (Intermediate – Advanced) (MLIAPR)
4th December 2023
Course Format
Pre-Recorded
Course Details
By the end of this course, students should be able to:
- Understand and implement advanced supervised learning techniques such as CNNs, RNNs, Transformer Models, and Bayesian Machine Learning methods.
- Understand and implement advanced unsupervised learning techniques including various clustering, dimension reduction, and anomaly detection methods.
- Apply these techniques to real-world datasets and interpret the results.
- Understand the underlying methods and assumptions/drawbacks of these techniques.
Intended Audiences
Venue
Course Information
Duration – 4 days
Contact hours – Approx. 28 hours
ECT’s – Equal to 3 ECT’s
Language – English
Teaching Format
Assumed quantitative knowledge
Assumed computer background
Equipment and software requirements
A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers.
Participants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).
A large monitor and a second screen, although not absolutely necessary, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students.
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.
COURSE PROGRAMME
Day 1
DAY 1
Deep Dive into Supervised Learning
We begin with an introduction to Deep Learning in which we cover the basic concepts and its difference from traditional machine learning. We then extend to Convolutional Neural Networks (CNNs), exploring their architecture, their use in image and video processing, and their role in object detection and recognition. Finally we cover time series models through Recurrent Neural Networks (RNNs) and their application in sequential data analysis and natural language processing.
In the afternoon sessions we implement CNNs and RNNs using real data sets
R Packages used: keras, tensorflow
Day 2
DAY 2
Advanced Supervised Learning Techniques
On day 2 we cover Transformer models and Bayesian machine learning techniques. We start by understanding the transformer architecture, its self-attention mechanism, and its use in natural language processing tasks. We then cover the basics of Bayesian inference and explore its use in classification and regression tasks, and compare it to traditional machine learning methods.
In the afternoon sessions the students can choose whether they explore either the Transformer or Bayesian methods further by following and extending some example R scripts.
R Packages: keras, tensorflow, rstan, brms, BART
Day 3
DAY 3
Unsupervised Learning – Clustering and Dimension Reduction
The third day will focus on advanced clustering techniques and dimension reduction. We start by exploring clustering techniques including hierarchical clustering, DBSCAN, and their use in segmentation. We then cover dimension reduction techniques; starting with PCA and extending to t-SNE and UMAP. We explain how these techniques work and explore their use in visualisation of data sets with high dimensions.
In the afternoon session students will explore the use of these techniques through real-world data sets.
R Packages: cluster, dbscan, factoextra, Rtsne, umap
Day 4
DAY 4
Unsupervised Learning – Anomaly Detection and Course Wrap-up
On the final day we will focus on anomaly detection techniques and bringing together the topics covered throughout the course. We start with various anomaly detection techniques and demonstrate their use in e.g. fraud detection, network security, and health monitoring. We then provide a discussion session where we review the content of the course and talk about future steps in Machine Learning.
In the afternoon students have the opportunity to work on their own data sets and ask questions of the course instructor.
R Packages: anomalize, forecast, e1071
